Assessing glacier melt contribution to streamflow at Universidad Glacier, central Andes of Chile

Glacier melt is an important source of water for high Andean rivers in central Chile, especially in dry years, when it can be an important contributor to flows during late summer and autumn. However, few studies have quantified glacier melt contribution to streamflow in this region. To address this shortcoming, we present an analysis of meteorological conditions and ablation for Universidad Glacier, one of the largest valley glaciers in the central Andes of Chile at the head of the Tinguiririca River, for the 2009–2010 ablation season. We used meteorological measurements from two automatic weather stations installed on the glacier to drive a distributed temperature-index and runoff routing model. The temperature-index model was calibrated at the lower weather station site and showed good agreement with melt estimates from an ablation stake and sonic ranger, and with a physically based energy balance model. Total modelled glacier melt is compared with river flow measurements at three sites located between 0.5 and 50 km downstream. Universidad Glacier shows extremely high melt rates over the ablation season which may exceed 10 m water equivalent in the lower ablation area, representing between 10 and 13 % of the mean monthly streamflow at the outlet of the Tinguiririca River Basin between December 2009 and March 2010. This contribution rises to a monthly maximum of almost 20 % in March 2010, demonstrating the importance of glacier runoff to streamflow, particularly in dry years such as 2009–2010. The temperature-index approach benefits from the availability of on-glacier meteorological data, enabling the calculation of the local hourly variable lapse rate, and is suited to high melt regimes, but would not be easily applicable to glaciers further north in Chile where sublimation is more significant.

Abstract licence: CC BY

62 more references, whose wording the paper’s licence keeps private.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.